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High-throughput neuroimaging-genetics computational infrastructure.

Ivo D Dinov1, Petros Petrosyan2, Zhizhong Liu2

  • 1Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, University of Southern California Los Angeles, CA, USA ; Biomedical Informatics Research Network, Information Sciences Institute, University of Southern California Los Angeles, CA, USA ; Statistics Online Computational Resource, University of Michigan, UMSN Ann Arbor, MI, USA.

Frontiers in Neuroinformatics
|May 6, 2014
PubMed
Summary
This summary is machine-generated.

Neuroscience research faces data challenges. A new high-throughput neuroimaging-genetics infrastructure at USC integrates data management, processing, and visualization for complex studies, aiding disease research.

Keywords:
Alzheimer's diseaseagingbig datacomputation solutionsgeneticsneuroimagingpipelinevisualization

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Area of Science:

  • Neuroscience
  • Computational Biology
  • Genetics

Background:

  • Neuroscientific research faces significant hurdles in managing, processing, mining, and interpreting large datasets.
  • Effective infrastructure is crucial for advancing scientific methods, computational resources, and translational healthcare findings.

Purpose of the Study:

  • To describe a novel high-throughput neuroimaging-genetics computational infrastructure.
  • To showcase its capabilities in integrating data management, processing, and visualization for complex research protocols.

Main Methods:

  • The infrastructure integrates data acquisition, archival, processing, mining, and interpretation.
  • It utilizes ultra-high-field and standard-field MRI scanners and an imaging-genetics database.
  • The Pipeline environment, a client-server architecture, enables graphical design, execution, and monitoring of workflows via portable XML objects.

Main Results:

  • The infrastructure supports heterogeneous neuroimaging, genetics, clinical, and phenotypic data.
  • It provides a suite of software tools for image analysis, modeling, genomic processing, and visualization.
  • Demonstrated translational applications using Alzheimer's and Parkinson's disease data.

Conclusions:

  • The developed infrastructure addresses key challenges in contemporary neuroscientific research.
  • It facilitates efficient and reproducible analysis of complex neuroimaging and genetic datasets.
  • This integrated system supports the advancement of translational neuroscience and healthcare findings.